Evidence map›Paper›PMID 31362397›Full record

ReviewPolymers2019

Parametric Analysis of Electrical Conductivity of Polymer-Composites.

Oladipo Folorunso, Yskandar Hamam, Rotimi Sadiku, Suprakas Sinha Ray, Adekoya Gbolahan Joseph

Open access · goldAbstract readReview
In one paragraph

Review in Polymers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.8field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 67 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Machine Learning in Polymer Research.Advanced materials (Deerfield Beach, Fla.) · 2025
    Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Oladipo FolorunsoFrench South African Institute of Technology (F'SATI)/Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa. oladfol2013@gmail.com.ORCID 0000-0002-8123-580X
Yskandar HamamFrench South African Institute of Technology (F'SATI)/Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa.ORCID 0000-0002-9673-9189
Rotimi SadikuInstitute of Nanoengineering Research (INER)/Department of Chemical, Metallurgy and Material Engineering, Tshwane University of Technology, Pretoria 0001, South Africa.ORCID 0000-0002-8504-1041
Suprakas Sinha RayDST-CSIR National Centre for Nanostructured Materials, Council for Scientific and Industrial Research, Pretoria 0001, South Africa.ORCID 0000-0002-0007-2595
Adekoya Gbolahan JosephInstitute of Nanoengineering Research (INER)/Department of Chemical, Metallurgy and Material Engineering, Tshwane University of Technology, Pretoria 0001, South Africa.ORCID 0000-0001-6381-0914
Tshwane University of Technology · ZACouncil for Scientific and Industrial Research · ZAUniLaSalle Amiens (ESIEE-Amiens) · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The problem associated with mixtures of fillers and polymers is that they result in mechanical degradation of the material (polymer) as the filler content increases. This problem will increase the weight of the material and manufacturing cost. For this reason, experimentation on the electrical conductivities of the polymer-composites (PCs) is not enough to research their electrical properties; models have to be adopted to solve the encountered challenges. Hitherto, several models by previous researchers have been developed and proposed, with each utilizing different design parameters. It is imperative to carry out analysis on these models so that the suitable one is identified. This paper indeed carried out a comprehensive parametric analysis on the existing electrical conductivity models for polymer composites. The analysis involves identification of the parameters that best predict the electrical conductivity of polymer composites for energy storage, viz: (batteries and capacitor), sensors, electronic device components, fuel cell electrodes, automotive, medical instrumentation, cathode scanners, solar cell, and military surveillance gadgets applications. The analysis showed that the existing models lack sufficient parametric ability to determine accurately the electrical conductivity of polymer-composites.

Indexed as

conductivityelectricalfillersmodelspercolationpolymers

Identifiers

PMID31362397
PMCPMC6722708
OpenAlexW2966697615

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.